Algorithmia Blog - Deploying AI at scale

Issue 45
This week we check out Y Combinator’s new track for companies applying AI to factories, take a deep dive into the lasted autonomous car news from Nvidia and Uber, and relay our favorite reads from the week.

Level 4 autonomous vehicles are considered “fully autonomous,” but are limited to the scope of driving scenario – long-haul trucking in this instance.

Uber’s 43 self-driving cars crossed 20,000 mile last week. The cars drove an average of .8 miles before the human driver had to take over for one reason or another.The good news is the number of miles between these “critical” interventions has improved to approximately 200 miles between incidents.

Self-driving cars have a spinning-laser problem. Lidar sensors are considered essential for building self-driving cars and pretty much everybody but Tesla is using them. But they’re bulky. And expensive. Just look at how ridiculous this thing is. Experts say that the sensors need improvements to better map a vehicle’s environment in 3-D.

What We’re Reading 📚

The Mobile Internet Is Over. Baidu Goes All In on AI. The Chinese company has more than 1,300 people working on tech like deep learning.(Bloomberg)

The body is the missing link for truly intelligent machines. In ways that we’re only just beginning to understand, our body and brain, from the cellular level upwards, have already built a model of the world that we can apply almost instantly to a wide array of challenges. (Aeon)

Appreciating Art with Algorithms. Let’s use a deep neural network to extract the style of an artist’s painting and then apply those features to an arbitrary photo. (Hacker Noon)

How DeepMind’s Memory Trick Helps AI Learn Faster. While AI systems can match many human capabilities, they take 10 times longer to learn. Now, by copying the way the brain works, Google DeepMind has built a machine that is closing the gap. (MIT Technology Review)